In mathematics, the Brascamp–Lieb inequality can refer to two inequalities. The first is a result in geometry concerning integrable functions on n-dimensional Euclidean space . It generalizes the Loomis–Whitney inequality and Hölder's inequality. The second is a result of probability theory which gives a concentration inequality for log-concave probability distributions. Both are named after Herm Jan Brascamp and Elliott H. Lieb.
The geometric inequality
Fix natural numbers m and n. For 1 ≤ i ≤ m, let ni ∈ N and let ci > 0 so that
Choose non-negative, integrable functions
Then the following inequality holds:
where D is given by
Another way to state this is that the constant D is what one would obtain by restricting attention to the case in which each is a centered Gaussian function, namely .
Relationships to other inequalities
The geometric Brascamp–Lieb inequality
For i = 1, ..., m, let ci > 0 and let ui ∈ Sn−1 be a unit vector; suppose that ci and ui satisfy
for all x in Rn. Let fi ∈ L1(R; [0, +∞]) for each i = 1, ..., m. Then
The geometric Brascamp–Lieb inequality follows from the Brascamp–Lieb inequality as stated above by taking ni = 1 and Bi(x) = x · ui. Then, for zi ∈ R,
It follows that D = 1 in this case.
As another special case, take ni = n, Bi = id, the identity map on , replacing fi by f1/ci
i, and let ci = 1 / pi for 1 ≤ i ≤ m. Then
The concentration inequality
Consider a probability density function . is said to be a log-concave measure if the function is convex. Such probability density functions have tails which decay exponentially fast, so most of the probability mass resides in a small region around the mode of . The Brascamp–Lieb inequality gives another characterization of the compactness of by bounding the mean of any statistic .
Formally, let be any derivable function. The Brascamp–Lieb inequality reads:
Relationship with other inequalities
The Brascamp–Lieb inequality is an extension of the Poincaré inequality which only concerns Gaussian probability distributions.
The Brascamp–Lieb inequality is also related to the Cramér–Rao bound. While Brascamp–Lieb is an upper-bound, the Cramér–Rao bound lower-bounds the variance of . The expressions are almost identical:
Further reference for both points can be found in "Log-concavity and strong log-concavity: A review", by A. Saumard and J. Wellner.
- This inequality is in Lieb, E. H. (1990). "Gaussian Kernels have only Gaussian Maximizers". Inventiones Mathematicae. 102: 179–208. doi:10.1007/bf01233426.
- This was derived first in Brascamp, H. J.; Lieb, E. H. (1976). "Best Constants in Young's Inequality, Its Converse and Its Generalization to More Than Three Functions". Adv. Math. 20: 151–172. doi:10.1016/0001-8708(76)90184-5.
- Ball, Keith M. (1989). "Volumes of Sections of Cubes and Related Problems". In Lindenstrauss, J.; Milman, V. D. Geometric Aspects of Functional Analysis (1987–88). Lecture Notes in Math. 1376. Berlin: Springer. pp. 251–260.
- This theorem was originally derived in Brascamp, H. J.; Lieb, E. H. (1976). "On Extensions of the Brunn–Minkowski and Prékopa–Leindler theorems, including inequalities for log concave functions, and with an application to the diffusion equation". Journal of Functional Analysis. 22: 366–389. doi:10.1016/0022-1236(76)90004-5. Extensions of the inequality can be found in Hargé, Gilles (2008). "Reinforcement of an Inequality due to Brascamp and Lieb". Journal of Functional Analysis. 254: 267–300. doi:10.1016/j.jfa.2007.07.019 and Carlen, Eric A.; Cordero-Erausquin, Dario; Lieb, Elliott H. (2013). "Asymmetric Covariance Estimates of Brascamp-Lieb Type and Related Inequalities for Log-concave Measures". Annales de l'Institut Henri Poincaré B. 49: 1–12. doi:10.1214/11-aihp462.